K-factor (viral coefficient)
K-factor, also called the viral coefficient, is the average number of new users each existing user generates through invitations or sharing. It measures self-propagation: whether your product grows partly under its own power, or relies entirely on paid acquisition. A K-factor above 1 means each user, on average, brings in more than one new user — the threshold for true viral growth.
How it is calculated
K-factor combines how much people invite with how well those invites convert:
K = invitations sent per user × conversion rate per invitation
So if each user sends 4 invites and 25% convert, K = 4 × 0.25 = 1.0. The two inputs are independent levers: you can raise K by prompting more shares or by making the invite landing experience convert better. Because both happen over time, K is usually measured per cohort over a defined window, not as a single instantaneous number.
Why it matters
The value of K relative to 1 changes everything. Below 1, virality amplifies paid acquisition — each paid user brings a few extra free ones, lowering your effective CAC — but growth still stops when you stop spending. At or above 1, growth becomes self-sustaining and can compound exponentially until the addressable audience saturates. Even a modest K (say 0.5) is hugely valuable: it can cut blended acquisition cost substantially. K also interacts with cycle time — how fast an invite turns into a new active user — since a high K with a slow cycle still grows slowly.
In games and apps
In games, K-factor is driven by social features: invite-a-friend rewards, multiplayer that needs friends, shareable scores and referral bonuses. Teams instrument the full invite loop as events — invite sent, invite opened, install, activation — so they can see exactly where the loop leaks and which incentives lift conversion. A strong K paired with healthy retention is the rare combination that produces breakout growth; a high K with poor retention just churns users in and out.
In Keentics
Keentics lets you instrument the invite loop as raw events and measure K per cohort by tracking invites sent, accepted and converted to activation. You can segment K by channel, country and campaign, watch how incentive changes move it, and pair it with retention and user path analysis to find where the viral loop breaks. Explore funnel analysis to map the invite-to-activation steps end to end.
Related: Active users · ARPPU · ARPU · Attribution